## For examples skipped in testing because they need recommended packages.

## This is skipped entirely on a Unix-alike if recommended packages are,
## so for Windows
if(!require("MASS")) q()

pdf("reg-examples-3.pdf", encoding = "ISOLatin1.enc")

## From datasets
if(require("survival")) {
  model3 <- clogit(case ~ spontaneous+induced+strata(stratum), data = infert)
  print(summary(model3))
  detach("package:survival", unload = TRUE)  # survival (conflicts)
}


## From grDevices
x1  <- matrix(rnorm(1e3), ncol = 2)
x2  <- matrix(rnorm(1e3, mean = 3, sd = 1.5), ncol = 2)
x   <- rbind(x1, x2)

dcols <- densCols(x)
graphics::plot(x, col = dcols, pch = 20, main = "n = 1000")


## From graphics:
## A largish data set
set.seed(123)
n <- 10000
x1  <- matrix(rnorm(n), ncol = 2)
x2  <- matrix(rnorm(n, mean = 3, sd = 1.5), ncol = 2)
x   <- rbind(x1, x2)

oldpar <- par(mfrow = c(2, 2))
smoothScatter(x, nrpoints = 0)
smoothScatter(x)

## a different color scheme:
Lab.palette <- colorRampPalette(c("blue", "orange", "red"), space = "Lab")
smoothScatter(x, colramp = Lab.palette)

## somewhat similar, using identical smoothing computations,
## but considerably *less* efficient for really large data:
plot(x, col = densCols(x), pch = 20)

## use with pairs:
par(mfrow = c(1, 1))
y <- matrix(rnorm(40000), ncol = 4) + 3*rnorm(10000)
y[, c(2,4)] <-  -y[, c(2,4)]
pairs(y, panel = function(...) smoothScatter(..., nrpoints = 0, add = TRUE))

par(oldpar)


## From stats
# alias.Rd
op <- options(contrasts = c("contr.helmert", "contr.poly"))
npk.aov <- aov(yield ~ block + N*P*K, npk)
alias(npk.aov)
options(op)  # reset

# as.hclust.Rd
if(require("cluster", quietly = TRUE)) {# is a recommended package
  set.seed(123)
  x <- matrix(rnorm(30), ncol = 3)
  hc <- hclust(dist(x), method = "complete")
  ag <- agnes(x, method = "complete")
  hcag <- as.hclust(ag)
  ## The dendrograms order slightly differently:
  op <- par(mfrow = c(1,2))
  plot(hc) ;  mtext("hclust", side = 1)
  plot(hcag); mtext("agnes",  side = 1)
  detach("package:cluster")
}

# confint.Rd
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3, 1, 9); treatment <- gl(3, 3)
glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())
confint(glm.D93)
confint.default(glm.D93)  # based on asymptotic normality}

# contrasts.Rd
utils::example(factor)
fff <- ff[, drop = TRUE]  # reduce to 5 levels.
contrasts(fff) <- contr.sum(5)[, 1:2]; contrasts(fff)

## using sparse contrasts: % useful, once model.matrix() works with these :
ffs <- fff
contrasts(ffs) <- contr.sum(5, sparse = TRUE)[, 1:2]; contrasts(ffs)
stopifnot(all.equal(ffs, fff))
contrasts(ffs) <- contr.sum(5, sparse = TRUE); contrasts(ffs)

# glm.Rd
utils::data(anorexia, package = "MASS")

anorex.1 <- glm(Postwt ~ Prewt + Treat + offset(Prewt),
                family = gaussian, data = anorexia)
summary(anorex.1)

# logLik.Rd
utils::data(Orthodont, package = "nlme")
fm1 <- lm(distance ~ Sex * age, Orthodont)
logLik(fm1)
logLik(fm1, REML = TRUE)

# nls.Rd
od <- options(digits=5)
## The muscle dataset in MASS is from an experiment on muscle
## contraction on 21 animals.  The observed variables are Strip
## (identifier of muscle), Conc (Cacl concentration) and Length
## (resulting length of muscle section).
utils::data(muscle, package = "MASS")

## The non linear model considered is
##       Length = alpha + beta*exp(-Conc/theta) + error
## where theta is constant but alpha and beta may vary with Strip.

with(muscle, table(Strip)) # 2, 3 or 4 obs per strip

## We first use the plinear algorithm to fit an overall model,
## ignoring that alpha and beta might vary with Strip.

musc.1 <- nls(Length ~ cbind(1, exp(-Conc/th)), muscle,
              start = list(th = 1), algorithm = "plinear")
## IGNORE_RDIFF_BEGIN
summary(musc.1)
## IGNORE_RDIFF_END

## Then we use nls' indexing feature for parameters in non-linear
## models to use the conventional algorithm to fit a model in which
## alpha and beta vary with Strip.  The starting values are provided
## by the previously fitted model.
## Note that with indexed parameters, the starting values must be
## given in a list (with names):
b <- coef(musc.1)
musc.2 <- nls(Length ~ a[Strip] + b[Strip]*exp(-Conc/th), muscle,
              start = list(a = rep(b[2], 21), b = rep(b[3], 21), th = b[1]))
## IGNORE_RDIFF_BEGIN
summary(musc.2)
## IGNORE_RDIFF_END
options(od)

# princomp.Rd
## Robust:
(pc.rob <- princomp(stackloss, covmat = MASS::cov.rob(stackloss)))

# termplot.R
library(MASS)
hills.lm <- lm(log(time) ~ log(climb)+log(dist), data = hills)
termplot(hills.lm, partial.resid = TRUE, smooth = panel.smooth,
        terms = "log(dist)", main = "Original")
termplot(hills.lm, transform.x = TRUE,
         partial.resid = TRUE, smooth = panel.smooth,
	 terms = "log(dist)", main = "Transformed")

# xtabs.Rd
if(require("Matrix")) {
 ## similar to "nlme"s  'ergoStool' :
 d.ergo <- data.frame(Type = paste0("T", rep(1:4, 9*4)),
                      Subj = gl(9, 4, 36*4))
 print(xtabs(~ Type + Subj, data = d.ergo)) # 4 replicates each
 set.seed(15) # a subset of cases:
 print(xtabs(~ Type + Subj, data = d.ergo[sample(36, 10), ], sparse = TRUE))

 ## Hypothetical two-level setup:
 inner <- factor(sample(letters[1:25], 100, replace = TRUE))
 inout <- factor(sample(LETTERS[1:5], 25, replace = TRUE))
 fr <- data.frame(inner = inner, outer = inout[as.integer(inner)])
 print(xtabs(~ inner + outer, fr, sparse = TRUE))
}


## From splines
library(splines)
Matrix::drop0(zapsmall(6*splineDesign(knots = 1:40, x = 4:37, sparse = TRUE)))
